YOLOv8s – EO Drone Detection (IRIS Benchmark)
Overview
This model is a YOLOv8s object detection model trained as part of the IRIS EO Drone Detection Benchmark.
It represents a baseline model within a controlled evaluation of multiple detection architectures under identical conditions. The purpose of this model is to capture behavior under real-world constraints such as long-range detection, small object scale, and environmental variability, not to represent peak or optimized performance.
Benchmark Reference
This model was trained and evaluated within the IRIS EO Drone Detection Benchmark.
👉 Primary Benchmark (Methodology, Evaluation, Comparison):
[Benchmark]
The benchmark defines:
- dataset construction
- annotation methodology
- evaluation protocol
- cross-architecture comparison
This model should be interpreted only within that context.
Model Details
- Architecture: YOLOv8s
- Task: Object Detection (Drone)
- Input Modality: EO imagery (RGB)
- Dataset: IRIS benchmark dataset (~1,000 validated annotations)
- Classes: 1 (drone)
Uses
Direct Use
This model is intended for:
- benchmark reference
- comparative evaluation
- analysis of model behavior under constrained datasets
Out-of-Scope Use
This model is not intended for:
- production deployment without further validation
- safety-critical detection systems
- environments significantly different from the benchmark dataset
Training Details
Training Data
The model was trained on a curated EO drone detection dataset derived from the Anti-UAV dataset: https://anti-uav.github.io/dataset/
The dataset was constructed using similarity-based candidate discovery and human-in-the-loop validation. It contains approximately 1,000 validated annotations and is designed to emphasize:
- long-range detection
- small object scale
- environmental variability
Full dataset construction details are available in the benchmark repository.
Training Procedure
The model uses a YOLOv8s architecture and was trained under aligned conditions with other evaluated models.
Training and inference were executed using a standardized YOLO-based interface for reproducibility and consistency.
IRIS is not tied to a specific model framework. This model is one artifact produced within a broader workflow for dataset development, evaluation, and architecture comparison.
Evaluation
This model was evaluated as part of the IRIS EO Drone Detection Benchmark.
The benchmark includes:
- standardized dataset splits
- aligned preprocessing
- cross-architecture comparison
- both quantitative metrics and qualitative inspection
Evaluation results and analysis are maintained in the benchmark repository as the source of truth.
Observed Behavior (Summary)
Within the benchmark:
- Provided consistent and stable detection behavior across conditions
- Served as a reliable baseline for comparison against other architectures
- Demonstrated reduced sensitivity at long range and small object scale relative to transformer-based approaches
Limitations
- Dataset size is intentionally constrained (~1,000 annotations)
- Performance reflects early-stage dataset development
- May underperform in:
- long-range detection scenarios
- low visibility conditions
- unseen environments
Model behavior is highly dependent on operating conditions and dataset composition.
License
This model is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.
About IRIS
IRIS is a visual intelligence platform focused on:
- similarity-driven dataset development
- human-in-the-loop validation
- controlled model comparison across architectures
- lifecycle-aware computer vision systems
This model represents one artifact within that workflow.
Check out the IRIS webpage for all the latest news and updates!
Model tree for IRIS-Computer-Vision/YOLOv8s_EO_Drone_Detection
Base model
Ultralytics/YOLOv8